Biroğul, Serdar2020-05-012020-05-0120162193-567X2191-4281https://doi.org/10.1007/s13369-015-1869-5https://hdl.handle.net/20.500.12684/6254WOS: 000371252700010Genetic algorithms (GA) has been used as a successful algorithm for many problems. GA has been redesigned with different methods or used in hybrid algorithms to solve different problems and improve solutions. In this study, epigenetic algorithm (EGA) design has been made by adapting epigenetic concepts to the classical GA structure. GA is counted as a heuristic research algorithm, and there is randomness in the function of genetic operators. However, owing to some serious research in medical field, it has been shown that through the epigenetics, randomness of crossover and mutation operators can be defined. With regards to this information in the field of medicine, in this study design of EGA, how epicrossover, epimutation operators, and epigenetic factors are made and how they do work and also how the epigenetic inheritance is possible have been told. Our designed EGA has been applied on base stations' BCCH frequency planning in GSM network that is a constrained optimization problem. Real base station's data have been used in solving the problem. EGA and GA coding have been made by using C# programming. In order to analyze the success of EGA than the classical GA, both algorithms have been used in solving of this problem. Because of this, EGA gave better results in a shorter time and less iteration than classical GA's.en10.1007/s13369-015-1869-5info:eu-repo/semantics/closedAccessEpigeneticEpigenetic algorithmEpicrossoverEpimutationEpi-inheritanceGenetic algorithmOptimizationFrequency planningArticle413883896WOS:000371252700010Q1Q3